def __init__(self,
user_id,
trainloader,
testloader,
model_name: str,
lr=3e-4,
epoch=1,
seed=123,
lr_decay=0.99,
decay_step=200):
BASE.__init__(self,
algorithm='fedsp',
seed=seed,
epoch=epoch,
model_name=model_name,
lr=lr,
lr_decay=lr_decay,
decay_step=decay_step)
self.user_id = user_id
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.trainloader = trainloader
self.testloader = testloader
self.loss_list = []
self.acc_list = []
def __init__(self,
seed=123,
rounds=20,
epoch=1,
clients_per_round=1,
eval_interval=1,
dataset_name='femnist',
model_name='femnist',
lr=3e-4,
batch_size=1,
lr_decay=0.99,
decay_step=200,
note=''):
BASE.__init__(self,
algorithm='fedper',
seed=seed,
epoch=epoch,
model_name=model_name,
lr=lr,
batch_size=batch_size,
lr_decay=lr_decay,
decay_step=decay_step)
self.model_name = model_name
self.dataset_name = dataset_name
self.base_params = self.model.base.state_dict()
self.updates = []
self.selected_clients = []
self.clients_per_round = clients_per_round
self.rounds = rounds
self.eval_interval = eval_interval
self.note = note
self.optim = {
'round': 0,
'acc': -1.0,
'ft-acc': -1.0,
'base_params': None,
'loss': 10e8,
'ft-loss': 10e8
}
self.train_writer = SummaryWriter(
f'/home/tdye/Fed101/visualization/fedper/{dataset_name}_{model_name}_C{clients_per_round}_E{epoch}_B{batch_size}_lr{lr}_train_{note}'
)
self.test_writer = SummaryWriter(
f'/home/tdye/Fed101/visualization/fedper/{dataset_name}_{model_name}_C{clients_per_round}_E{epoch}_B{batch_size}_lr{lr}_val_{note}'
)
self.clients = self.setup_clients()
assert self.clients_per_round <= len(self.clients)
self.surrogates = self.setup_surrogates()
assert len(self.surrogates) == clients_per_round
